ArticleStatistics in medicine2026
Semantic Similarity-Informed Bayesian Borrowing for Quantitative Signal Detection of Adverse Events.
Article in Statistics in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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5 authors.
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Abstract
We present a Bayesian dynamic borrowing approach to enhance quantitative identification of adverse events in spontaneous reporting systems. The method embeds a robust meta-analytic predictive prior and incorporates semantic similarity measures (SSMs) to enable weighted information sharing from clinically similar MedDRA preferred terms (PTs) to the target PT. This continuous similarity-based borrowing overcomes limitations of rigid hierarchical grouping in disproportionality analysis (DPA). Using data from the FDA Adverse Event Reporting System and a time-stamped reference set, we evaluate our approach-termed IC SSM-against the current standard approach of "traditional" information component (IC) analysis and borrowing at the MedDRA HLGT level (IC HLGT). The primary IC SSM approach at the standard threshold (2.5% posterior quantile
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